Exactly ten years ago, at an early Quantified Self meetup, Joe Betts-La Croix expressed “three wishes” for tools to make data collection for self-tracking easier.

Joe asked for:

A simple database that would accept data inputs from anybody using fairly simple and adaptable formats (for instance .xml) and just hold it there, eventually allowing other people to upload tools for analysis so we could do interesting things with the data, like run statistical analysis, or simply graph it. Joe wanted something extremely simple.

Devices that will send data to a simple, private data store using wifi. For illustration, he describes a scale that will send his weight data automatically, a blood pressure monitor, and a blood glucose monitor.

An app for his phone designed to allow quick manual entry of any value, which is then transferred to the data store.

It’s interesting to look back on this from exactly ten years and ask: To what extent have these wishes come true? What turned out to be the hardest problem?

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The Quantified Self Conference was held on September 22nd and 23rd in Portland, Oregon. Over the two days of the conference we had over eighty talks, presentations, and breakout discussions about self-tracking, everyday science, and “self-knowledge through numbers.” Over the next few weeks we will be posting videos, slides and notes, but for now let us just say thank you so much to everybody who attended and made this meeting possible. To see QS18 related posts on Twitter, look for #QS18.

Surprisingly little of the attention and funding turned to personalized, predictive, preventative medicine has focused on the female reproductive system: pregnancy onset cannot be quickly identified, menopause onset and trajectory remain entirely mysterious, and adverse reactions to tools like hormonal birth control are difficult to anticipate. Importantly, there are no automated, cheap, high-accuracy methods for predicting ovulation in the diverse population of cycling people. Existing methods are based on once-daily measurement of basal body temperature (BBT). These are prone to sampling error, unreliable for people with irregular ovulatory cycles, and make use of only one minute out of the 1440 available as data points per day. Although some pricey new devices aim to tackle the pitfalls of BBT via more frequent measurement, they mostly hold their analytical techniques as company secrets, making their estimations difficult to understand or trust.

So, if you are trying to get pregnant, trying to not get pregnant, or trying to understand anything else specific associated with your cycle, maybe you share my more general question: What can we learn about ourselves from continuously tracking body temperature across the cycle?

As part of my graduate research, and in collaboration with the Quantified Self community, I’m organizing a “participatory research project” on ovulatory cycling. To capture the uniqueness in individuals’ cycles, and to work towards predicting features of interest, we’ll measure continuous skin temperature, as well as luteinizing hormone (LH) and a range of other metrics. From my prior research, I’ve seen that mammals (including humans) have distinctive patterns of body temperature around ovulation, but that there are wide individual differences in the way temperature changes across the cycle. It’s my goal to characterize both the uniqueness of individuals and the common patterns preceding ovulation. I will of course release the analytical tools I build open-source. In this project, I’ll be requesting permission to work towards publishing results containing de-identified data from participants, but keep in mind that data sharing is completely optional; you’re still invited to join if you want to keep your data completely private.

We recently tested the plausibility of this style of participatory research in a year-long project exploring blood cholesterol variability. Thanks to research support from Amgen, participants had access to testing instruments that allowed them to measure their own cholesterol. We measured our blood cholesterol as often as hourly, and met every week or two to discuss our projects. We showed the work at QSPH 18, participants made a range of personal discoveries, and we generated two manuscripts, currently in review, to report our results.

If you think you might be interested in participating, we’re going to have a breakout discussion at QS18 called “Map Your Ovulatory Cycle With Continuous Body Temperature”. There, we’ll outline the proposed structure of the project and get a sense of what questions would be most interesting to ask with this data. I’d like to see if we can identify ovulation and menses onset, but there are many other possible topics. Are you interested in menopause? Pregnancy? Effects of disturbed sleep? Are you a non-cycling person who is interested in continuous, non-invasive biomarkers? If you can make it to QS18 in Portland, please come to our session and bring your questions!

Maggie Delano is a professor of engineering and very experienced self-tracker whose pioneering work on DIY measurements systems includes a fluid status monitor for patients with congestive heart failure and a wearable device that continuously measures single lead electrocardiogram (ECG) and three axis acceleration data for up to one week. She wrote the first Quantified Self code of conduct, organized a sponsorship program for QS15, and started the QS Boston Women’s Meetup.

In addition to her DIY hardware projects, Maggie has also spent time tracking her work efficiency, a project she’ll present at QS18. While completing her PhD dissertation, she wondered if she could make her time more efficient rather than just adding in more work hours during the day. She began using the the Pomodoro Method (25 minutes of work followed by a 5 minute break) and collected a very large amount of data that helped her learn about her personal work habits.

Her instrumentation included RescueTime to track her computer usage and Strict Workflow to cut out distractions during each pomodoro segment. After collecting this data over five years and redefining a successful work day in terms of distraction-free time instead of total time, she established a “cadence” for productivity. This deceptively simple project underlines the fact that the value of self-tracking doesn’t lie in complex tools or advanced technology, even for people who are expert engineers. To learn and practice a good working cadence, complex tools would be disruptive. RescueTime and Strict Workflow are relatively simple ways to create a record and restrict distractions, and this alone formed the basis of important learning.

Maggie will present her project and answer questions at QS18 Conference coming up in just two weeks. If you come, you can also ask her insanely deep questions about recording electrical signals from the body.

Esther Dyson is a board member of 23andme, former chair of ICANN and the Electronic Frontier Foundation, and an investor in companies like Omada Health, PatientsLikeMe, and Medspace. But, like the rest of us, she spends a good portion of her life unconscious. While sleeping, she collects data with three different devices: Oura, Whoop, and ResMed. Each device tracks slightly differently: the Oura tracks sleep via a ring worn on the finger, while the Whoop is a wrist-based tracker and the ResMed S+ sits near the bed and uses radio waves to detect body movement. Unsurprisingly, the devices don’t always agree.

In her upcoming Show&Tell talk at QS18, Esther will talk about why she uses three sleep tracking devices and how she interprets their disparate outputs, providing a close look at the type of questions that interest many of us when we’re starting a self-tracking project. What constitutes “accurate” data? What are the differences between supposedly reliable tools? From the ill-fated Zeo to the S+, Whoop, and Oura, sleep has been a notoriously difficult metric to track. Whether it is because of an inaccurate sensor or the difficulty of distinguishing sleep patterns of the target individual from surrounding noise, sleep has been much tougher to track than many other metrics. Dyson’s talk provides insight into how to deal with those difficulties and still create useful insights from sleep data.

By looking at data from multiple sensors, Dyson gives a picture of how data can change according to the instrument used to record it, but also points to a central feature of a successful self-tracking project: that is, to create useful, actionable conclusions in the context of one person’s life, answering questions not about the universally verifiable accuracy of sleep measurement, but about her own sleep.

You can check out the full schedule of show&tell talks, breakout discussions and how-to workshops at the 2018 Quantified Self Conference in Portland, Oregon.

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If you were to look at Aaron Parecki’s map of his hometown, there is only a slight chance you’d recognize it as Portland, Oregon. Some roads are brightly colored thick lines that stand out against a black background and others are thin, barely visible filaments that are easy to miss. There are no marked roads and no scale. It’s the last tool you’d want to use to get from point A to point B. Even so, in a different sense might be the most detailed and most personal map of Portland ever created, made from a four years of location data recorded at increments of 1-6 seconds. Aaron’s map of self-tracking data is a strong reminder that we interact with the places we live on our own terms that aren’t necessarily dictated by the roads in front of us.

Aaron’s movement in Portland from 2008 to 2012. Aaron has continued to track his location and now has ten years of data.

The image shows Aaron’s location from 2008 to 2012, but he has continued to log this information and has recently reached the ten year mark. The tools he’s used has changed over time, but they have increasingly mirrored the deeply personal element behind the maps. Aaron’s self-designed iOS app Overland passively tracks location while the tracking server Compassstores the data, making the actual recording process unobtrusive and creating an easy platform to retroactively analyze the data. Both are open source and allow other self trackers to modify the technology to their own projects. Likewise, Aaron has developed both the app and API with the intent of individuals owning their own data. Because location tracking includes not just the physical location of a person at any point in time, but also a detailed picture of their movement habits and areas of interest, both tools include privacy controls to make sure that the user retains ownership and access to the data.

A comprehensive record of location data can easily be mapped on to mood, biological markers, or any other data recorded at the same time. With these tools, it would be possible to see how passing through a specific area of town affects mood or if a particular commute correlates with a change in weight or heart rate, giving context to metrics on how our bodies are functioning that are normally dissociated from the surrounding environment.

Aaron’s project is a good map of his own experience of Portland and the tools he uses provide a blueprint for your own location-based tracking project. He’ll be leading a workshop at the QS Conference this month, so if you want to talk about how you can apply these approaches in your own projects, you’ll have a chance to meet him there.

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In May we released the Personal Data Notebooks with Open Humans. These interactive documents – which bring together text, images and code – are designed to easily access an individual’s own personal data. At the launch of the Personal Data Notebooks we invited the Open Humans and Quantified Self community to contribute their own personal data analyses, to share them with a wider audience. Thanks to the community our analysis library includes notebooks that make use of 23andMe, Fitbit, Twitter, Apple HealthKit, Moves, RescueTime and Google Search History!

To make it easier to find these notebooks – and easier to share your analyses with others – we are now launching our companion to the Personal Data Notebooks, the Personal Data Exploratory. The Exploratory lists ready-to-use data analyses which can be run on your own data as a Personal Data Notebook. You can browse the existing analyses, filter them by your own data sources of interest, preview them, and like/discuss them with other community members. Once you have found an analysis you would like to run on your own data, it is only two clicks to run it.

Your favorite data source is missing or you have an analysis you want to share with a wider audience? Sharing your own Personal Data Notebook and putting it into the Exploratory is just as easy and can be done right from an existing notebook. Give your notebook a descriptive name, write some words on what the analysis does and that’s it. Together we can create a thriving library of quantified self analyses.

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Please join us at QS18 for over 60 first person talks, tool demos, and expert-lead workshops about self-tracking, N-of-1, and everyday science. Our focus this year is on “QS&Learning.” Along with a special plenary talk and discussion by pioneering teacher, scholar and self-experimenter Alan Neuringer, we are bringing together Quantified Self experts from all over the world to share knowledge about what we are learning about ourselves with our own data, and how we can share this knowledge with our children, students, and peers.

We’ll also have a special focus on open tools. One of the most powerful forces driving QS in the last two years has been the energy of DIY and open hardware makers. Many uses of data for learning don’t match well with the business models of commercial device companies, who tend to be greedy about data they collect, focused on mainstream use cases, and enthralled by the (potential) money available from traditional health care. Where does this leave individuals and communities who want to learn right now, using tools that match our needs? When you jon us at this year’s conference, you’ll meet the people creating the next generation of open tools, and using them to rapidly accelerate learning about health, sports, environment, and education – among many other topics.

All the sessions listed on our QS18 Conference Program Page have been developed in collaboration with conference registrants. We’ll keep adding and changing up until the moment the conference starts. So please be in touch and tell us what you’re working on. You can share your ideas for sessions when you register.

For a preview, here are some of the QS Show&Tell Talks we’ve announced:

Tracking Across Generations – From Journals To Life-Logging GlassesAaron YihSince the day Aaron Yih was born, his grandfather documented his life in large picture collages he hung on the walls. Now that he’s 24 and his grandfather is 84, Aaron is using digital archiving and modern lifelogging tools to make a record of his grandfather’s extensive experiences.

What I’m Learning From My Meditation AppAlec RogersAlec Rogers wanted to see if there was a way to measure mindfulness after meditation. He’ll talk about this and other lessons he learned using data from a simple, open source meditation tracker that he wrote himself.

Using My Training Data To Inform My FashionAnna Franziska Michel
Anna Franziska Michel will describe her use of her own running and cycling data as material for her startling and beautiful work in fashion design.

Blood Values Beyond Ketones – The Effect Of Exercise, Fasting, And BathingBenjamin BestBenjamin Best has decades of experience with self-collected data. He’ll be talking about the analytical and graphical methods he uses to see the effects of exercise, fasting, bathing, and other common activities on his blood test values.

When Do I Do What I Say And How Does It Make Me Feel About LifeEli RickerEli Ricker tracks what he says he’s going to do and how often he does it. He’ll talk about he’s learned by connecting this data about his actions to his “life satisfaction” score.

3 Different Sleep Trackers Don’t Agree…. But What Can I Learn AnywayEsther DysonEsther Dyson is obsessed with time and circadian rhythms. Wanting to understand how she slept, she started with the Zeo long ago, but now uses the Oura, Whoop, and ResMed/Sleepscore simultaneously. But what happens when this data disagrees?

Using Step And Sleep Data To Monitor RecoveryJacqueline WheelwrightFitness and sleep trackers often contain built in assumptions about what’s optimal. Jacqueline Wheelwright describes how these data can be used for less common and more personal reasons.

My Headaches From Tracking HeadachesJakob Eg LarsenJakob Eg Larsen predicted tracking headaches would be an easy task. But the very first question turned out to be less straightforward than it seemed: What counts as a headache? He’ll show his data and talk about his learning process over 2.5 years.

Exercising Without Glucose Which Is Supposedly Impossible
Jessica Ching
Bay Area QS Show&Tell participants may remember Jessica Ching’s wonderful talk about training dogs to detect low blood sugars. This year she’ll show data about a different project: learning how exercise without glucose.

I Made Polyphasic Sleep Work For Me
Jonathan Berent
You’d have to be a crazy to think you could get by on 2.5 hours of sleep. Jonathan Berent is that kind of crazy. He’ll show data from his polyphasic sleeping, the effects this had on his life, and what he still hopes to discover.

Can Tracking Devices Detect And Help Me With Having Low Energy For An Extended Period?Justin LawlerJustin Lawler has been dealing with low energy for the past 6 months. An avid self-tracker, he wanted to see how well the currently available tools capture this feeling and help him along a path of improvement.

An N-of-2 Study with My Best Friend About How to Lower Blood PressureKarl Heilbron, Fah Sathirapongsasuti
With a family history of stroke and early warning signs of hypertension, Fah Sathirapongsasuti recruited friend and fellow scientist, Karl Heilbron, for a two person self-study of how lifestyle influences their blood pressure.

What InsideTracker Taught Me About My Five-Day FastKyrill PotapovKyrill Potapov tested theory that a fast can clear out the digestive tract and repopulate it differently. He shares his results from a 5 day fast, using InsideTracker panels to test his before and after states.

A Self-Study Of My Child’s Risk Of Intellectual Disability From A Rare Genetic Variant Carried By My FamilyMad BallMad Ball is a carrier for a rare genetic disease which entailed risk of having a child with a serious intellectual disability. But how much risk? Through careful self-investigation based on consumer genomics, a reasonable estimate turned out to be possible.

The Cost of Interruption
Madison Lukaczyk
Madison Lukaczyk tracked her time to see the impact that interruptions had on her productivity; the data and analysis changed how she uses her communication tools.

Learning From 5000 PomodorosMaggie DelanoMaggie Delano used the Pomodoro method – 25 minutes of work followed by 5 minutes of anything else – to complete her Ph.D. Her 5 years of Pomodoro data challenges the assumption that working all the time is the key to accomplishing things.

5 Years Of Tracking And Visualizing Posture Data
Esther Gokhale, Mark LeavittHow can a sensor accurately detect whether your back is aligned? Mark Leavitt and Esther Gokhale have been working on this problem for years and they share how they used their data to improve their posture.

Running Three Marathons On Zero CaloriesMikey SklarMikey Sklar ran three marathons in one day. He consumed liquids for hydration and metabolites but no calories. He’ll show how he used personal data to understand how this seemingly impossible feat could be accomplished.

Which Grasses Aggravate My AllergiesThomas Blomseth ChristiansenThomas Christiansen’s allergies are aggravated when running during grass pollen season. For this extremely clever project he used a GoPro to document passing vegetation and a device to record his sneezes in order to pinpoint what plants activated his nose.

Learning From My WhingesValerie LanardValerie Lanard keeps a detailed workout spreadsheet. In her “notes” section, she wrote out whatever excuse she had for not working out. Over time, she realized that this was a rich dataset on it’s own, detailing what’s happening when she’s not exercising.

Cholesterol Levels While NursingWhitney Erin BoeselAfter giving birth, Whitney Erin Boesel learned that her cholesterol was very high. Given her family history, it seemed that an intervention was in order. But what if she did nothing and simply made observations?

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Following closely behind Whitney’s pregnancy project, it is fitting to share Erica Forzani’s pregnancy tracking project that can inspire any human who has carried a human in her belly. In addition to just being pregnant and dealing with the work involved with growing a human, Erica tracked her blood glucose levels, physical dimensions, weight, resting metabolic rate, activity, blood pressure and diet throughout her pregnancy to argue many pregnancy and breastfeeding myths. There are a lot of them and her diligent work proves many false.

I’m not quite sure what it is, but for some reason, people love to give pregnant women and women with babies/kids feedback about whatever they are doing. Be it positive or negative, some people just can’t help offering some bit of information they are observing. Perhaps it is because, procreating is somehow instinctively shared among humankind, so people somehow feel they have a piece-in-the-game with the raising of any little being…whatever it is, Erica’s project politely and factually stomps out many of the myths people often hear while carrying a human.

After conceiving a beautiful baby girl, Whitney E. Boesel participated in the Bloodtester’s Project - a group of self-trackers conducting their own experiments to better understand their cholesterol together. After having her baby, Whitney learned that her cholesterol was unusually high and she became curious to understand what the cause was. She presented her findings, Cholesterol Variability: Hours, Days, And My Ovulatory Cycle, at the QS CVD Symposium earlier this year.

Given that one side of Whitney’s family genetics has very high cholesterol, she wondered if it was finally time she had to stop eating so much cheese, or if rather, it was simply high due to having a baby. Using an at home cholesterol testing device (Cardiochek), she decided to test a fairly unusual hypothesis: if she does absolutely nothing, will her cholesterol get better all by itself? After getting more cholesterol data points ever recorded of a woman post-birth, she happily discovers that her cholesterol did just get better as her body’s hormones shifted back to her own. She continues to track her cholesterol among other things and we look forward to hear what she learns next.

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